def _cov_sigma(self):
"""
Estimated covariance matrix of vech(sigma_u)
"""
D_K = tsa.duplication_matrix(self.neqs)
D_Kinv = npl.pinv(D_K)
sigxsig = np.kron(self.sigma_u, self.sigma_u)
return 2 * chain_dot(D_Kinv, sigxsig, D_Kinv.T)
def test_duplication_matrix():
for k in range(2, 10):
m = tools.unvech(np.random.randn(k * (k + 1) / 2))
Dk = tools.duplication_matrix(k)
assert (np.array_equal(vec(m), np.dot(Dk, vech(m))))
def test_duplication_matrix():
for k in range(2, 10):
m = tools.unvech(np.random.randn(k * (k + 1) / 2))
Dk = tools.duplication_matrix(k)
assert(np.array_equal(vec(m), np.dot(Dk, vech(m))))
